[Question]: How to update local index to cloud

[axz91]

Question Validation

• I have searched both the documentation and discord for an answer.

Question

build index

index = VectorStoreIndex(nodes)

save index to disk

index.set_index_id("vector_index")
index.storage_context.persist("./storage")

may i know how to update this local index to, for example Supabase Vector Store
https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/SupabaseVectorIndexDemo.html

[dosubot[bot]]

Answer generated by a 🤖

Hello,

Based on the context provided, it seems you want to update your local index to a cloud-based one, specifically the Supabase Vector Store.

In the LlamaIndex framework, there is a specific class designed to interact with Supabase Vector Store, named SupabaseVectorStore. This class allows you to initialize the SupabaseVectorStore with a connection string and collection name, convert llama filters to vecs filters, add embedding results to the index, and query the index for the top k most similar nodes.

Here is a code snippet on how you can use this class to update your local index to Supabase Vector Store:

from llama_index.vector_stores import SupabaseVectorStore
from llama_index.storage.storage_context import StorageContext

# create Supabase client and a new collection
supabase_vector_store = SupabaseVectorStore(postgres_connection_string, collection_name)
storage_context = StorageContext.from_defaults(vector_store=supabase_vector_store)

# build index
index = VectorStoreIndex(nodes, storage_context=storage_context)
# save index to disk
index.set_index_id("vector_index")
index.storage_context.persist("./storage")

In this code, replace postgres_connection_string with your actual Postgres connection string and collection_name with the name of the collection where you want to store the embeddings.

Please note that the delete method is not yet implemented in the SupabaseVectorStore class. If you need to delete a document from the index, you will need to implement this method.

For more information, you can refer to the SupabaseVectorStore class in the LlamaIndex codebase: llama_index/vector_stores/supabase.py

I hope this helps! If you have any other questions, feel free to ask.

Sources

• [Question]: Best way to copy a normal VectorStoreIndex into a ChromaDB.
• llama_index/tools/tool_spec/load_and_search/base.py
• llama_index/vector_stores/supabase.py

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